KECD 2310 - EMPIRICAL INDUSTRIAL ORGANIZATION

The aim of this course is to provide a solid understanding of the modern empirical tools useful to learn about strategic behaviour of firms, the structure of markets, and consumer demand. The questions of interest are empirical in nature, like: “How are consumers affected if two car manu-facturers would merge?” or “When banning short-distance flights, what is the effect on prices of remaining flights and of high-speed trains to nearby cities?”. To answer such questions, modern empirical organization uses a combination of theory, institutional details, and precise inference to convincingly answer such questions. The first part of the course focuses on the theoretical foundations, which include population choice theory and equilibrium price setting of firms in imperfectly competitive markets. The focus always remains empirical: what assumptions are made to allow for the estimation of realistic model primitives taking into account any data limitations that the econometrician might face. The second part of the course focuses on applying these insights in practice. We learn how to perform demand estimation on actual data in Matlab. We will cover data requirements, types of instruments, computation, and simulation. There will be a take-home exercise where you'll apply what you've learned to estimate a demand model in Python, Julia, Matlab, or R. The third part of the course places the covered material in a broader perspective. All students present one of the papers that have been influential in this field, either by extending the methodology to understand otherwise hard-to-quantify aspects of demand (like, demand for bundles of goods) or by applying it to improve real-world understanding of markets (like, using it to obtain market shares for hypothetical mergers). Overall, the course covers a wide range of applications in the empirical industrial organization literature and provides students with the skill set to perform thorough model-based empirical anal-ysis. There will be a final exam in class where students are evaluated on their understanding of modelling choices, estimation strategy, and empirical application of the methods learned in class.

Marleen MARRA

Séminaire

English

A basic understanding of: calculus (derivatives), statistics (regression), probability theory (e.g. what a probability distribution is), game-theory (e.g. what a Nash equilibrium is). An interest in under-standing people or firms making choices. No coding experience is required, this will be covered in class, although some prior experience with R / Python / Matlab / Julia etc. will be useful

Spring 2024-2025

Final exam: 40%, Take-home exercise: 25%, Presentation of journal article: 25%, Class participa-tion: 10%

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